The goal of this method is to generate the data needed by the task
and save it on disk for future uses, for example by the `setup` method.
The objective is to avoid systematically recreating data on each process
at the beginning of a training

Now all the segmentations tasks in `pyannote` inherit the `SegmentationTask`
(previously `SegmentationTaskMixin`), which inherits the `Task` class. This
commit also adds a `prepared_data` attribute  to the `Task` class. That
attribute is a dict which contains all the prepared data by the `prepare_data`
method.

…note-audio into feat/data_preparation

One for the test of the `MultiLabelSegmentation` task, and
the other for the test of the `SupervisedRepresentationLearningWithArcFace`
task.

This eliminates the need to reload pickle data in setup when in the main process

…cenarios

…note-audio into feat/data_preparation

This issue occured when a list of classes was  specified during `MultiLabelSegmentation`
instanciation.

* use npz archive instead pickle to save task data

* improve code readability

* improve(task): update numpy array dtypes

In order to use types whose size better machtes the contents of the arrays

* remove `end` entry from `annotated_regions` numpy array

This entry was redundant with the start and duration entries,
since `end` = `start` + `duration`.

* fix: allow data preparation to be finished when task has no validation

* improve: clear data lists after assignation to `self.prepared_data`

This is to avoid data redundancy in the `prepare_data` method

---------

Co-authored-by: clement-pages <[email protected]>